For the camera perspective,lighting,climate,landform and other cond itions of large changes or the existence of fast moving objects in the complex s cene,the accuracy and robustness of simultaneous Locali-zation and mapping (SLAM) are low,a closed-loop detection as a solution to SLAM pose an import a nt link of the drift,combined with this paper proposes a closed-loop detection method based on neural network.In this method,visual image data is obtained th rough sensors.Different from the traditional method of feature extraction,the improved triple constraint loss function is adopted in this paper to train Darkn et to extract image features and construct corresponding feature vector matrix.B ecause Darknet borrowed from the idea of residuals network (RESNET),it has a de ep network layer while maintaining a high accuracy rate,which greatly reduces t he error of feature extraction.Through the self-encoder method to reduce the di m ension of the data,through the cosine similarity calculation,set a reasonable threshold value,can get the closed-loop detection results faster.Last through t he two open visual SLAM closed loop testing data set,the new college data colle ction and light and the angle change is more obvious city centre data set on the experiment,the results show that the proposed method under complicated environ ment than the existing closed loop detection method,can get higher accuracy and speed,and better meet the visual SLAM system to eliminate the accumulated erro r and real-time requirements.